223 citations · 727 across the 52 of their papers we have counts for
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PhysDiff: Physics-Guided Human Motion Diffusion Model
Ye Yuan, Jiaming Song, Umar Iqbal +2
Denoising diffusion models hold great promise for generating diverse and realistic human motions. However, existing motion diffusion models largely disregard the laws of physics in…
Fast Sampling of Diffusion Models via Operator Learning
Hongkai Zheng, Weili Nie, Arash Vahdat +2
Diffusion models have found widespread adoption in various areas. However, their sampling process is slow because it requires hundreds to thousands of network evaluations to emulat…
eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers
Yogesh Balaji, Seungjun Nah, Xun Huang +10
Large-scale diffusion-based generative models have led to breakthroughs in text-conditioned high-resolution image synthesis. Starting from random noise, such text-to-image diffusio…
LION: Latent Point Diffusion Models for 3D Shape Generation
Xiaohui Zeng, Arash Vahdat, Francis Williams +4
Denoising diffusion models (DDMs) have shown promising results in 3D point cloud synthesis. To advance 3D DDMs and make them useful for digital artists, we require (i) high generat…
GENIE: Higher-Order Denoising Diffusion Solvers
Tim Dockhorn, Arash Vahdat, Karsten Kreis
Denoising diffusion models (DDMs) have emerged as a powerful class of generative models. A forward diffusion process slowly perturbs the data, while a deep model learns to graduall…
Differentially Private Diffusion Models
Tim Dockhorn, Tianshi Cao, Arash Vahdat +1
While modern machine learning models rely on increasingly large training datasets, data is often limited in privacy-sensitive domains. Generative models trained with differential p…
State-specific protein-ligand complex structure prediction with a multi-scale deep generative model
Zhuoran Qiao, Weili Nie, Arash Vahdat +2
The binding complexes formed by proteins and small molecule ligands are ubiquitous and critical to life. Despite recent advancements in protein structure prediction, existing algor…
Diffusion Models for Adversarial Purification
Weili Nie, Brandon Guo, Yujia Huang +3
Adversarial purification refers to a class of defense methods that remove adversarial perturbations using a generative model. These methods do not make assumptions on the form of a…